code: def forward(self, input, target, label):
      log_input_prob = F.log_softmax(input / self.T, dim=1)
      target_porb = F.softmax(target / self.T, dim=1)
      loss = F.kl_div(
          *flatten_probas(
              log_input_prob,
              target_porb,
              label,
              ignore=self.ignore_index,
          ),
          reduction="batchmean",
      )
      return self.T * self.T * loss
  
file_path: insightface/parsing/dml_csr/loss/kl_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the KLDivergenceLoss obj's underlying member method code with example usage: obj.forward(input, target, label) and returns: unknown